load_linnerud#

sklearn.datasets.load_linnerud(*, return_X_y=False, as_frame=False)[source]#

Load and return the physical exercise Linnerud dataset.

This dataset is suitable for multi-output regression tasks.

Samples total	20
Dimensionality	3 (for both data and target)
Features	integer
Targets	integer

Read more in the User Guide.

Parameters:

return_X_ybool, default=False: If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

Added in version 0.18.
as_framebool, default=False: If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

Added in version 0.23.

Returns:

dataBunch

Dictionary-like object, with the following attributes.

data{ndarray, dataframe} of shape (20, 3): The data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, dataframe} of shape (20, 3): The regression targets. If as_frame=True, target will be a pandas DataFrame.
feature_names: list: The names of the dataset columns.
target_names: list: The names of the target columns.
frame: DataFrame of shape (20, 6): Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.
DESCR: str: The full description of the dataset.
data_filename: str: The path to the location of the data.
target_filename: str: The path to the location of the target.

Added in version 0.20.

(data, target)tuple if return_X_y is True

Returns a tuple of two ndarrays or dataframe of shape (20, 3). Each row represents one sample and each column represents the features in X and a target in y of a given sample.

Added in version 0.18.

Examples

>>> from sklearn.datasets import load_linnerud
>>> linnerud = load_linnerud()
>>> linnerud.data.shape
(20, 3)
>>> linnerud.target.shape
(20, 3)